{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = pd.read_table('fruit_data_with_colors.txt')\n",
    "fruits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple' 'mandarin' 'orange' 'lemon']\n"
     ]
    }
   ],
   "source": [
    "print(fruits['fruit_name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(59, 7)\n"
     ]
    }
   ],
   "source": [
    "print(fruits.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.542373</td>\n",
       "      <td>163.118644</td>\n",
       "      <td>7.105085</td>\n",
       "      <td>7.693220</td>\n",
       "      <td>0.762881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.208048</td>\n",
       "      <td>55.018832</td>\n",
       "      <td>0.816938</td>\n",
       "      <td>1.361017</td>\n",
       "      <td>0.076857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.930000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fruit_label        mass      width     height  color_score\n",
       "count    59.000000   59.000000  59.000000  59.000000    59.000000\n",
       "mean      2.542373  163.118644   7.105085   7.693220     0.762881\n",
       "std       1.208048   55.018832   0.816938   1.361017     0.076857\n",
       "min       1.000000   76.000000   5.800000   4.000000     0.550000\n",
       "25%       1.000000  140.000000   6.600000   7.200000     0.720000\n",
       "50%       3.000000  158.000000   7.200000   7.600000     0.750000\n",
       "75%       4.000000  177.000000   7.500000   8.200000     0.810000\n",
       "max       4.000000  362.000000   9.600000  10.500000     0.930000"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the numerical values do not have the same scale. We will need to apply scaling to the test set that we computed for the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fruit type distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fruit_name\n",
      "apple       19\n",
      "lemon       16\n",
      "mandarin     5\n",
      "orange      19\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(fruits.groupby('fruit_name').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FcH1V3VBVvwD+CjhqWp+jgE90058GXpokI6xRkjTNOAJjX+BHffObu7aBfapqK3An8MSR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\nJElNDAxJUhMDQ5LU5P8DX8JG2PZsFvMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b5668aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(fruits['fruit_name'],label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is pretty balanced except mandarin. We will just have to go with it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plot for each numeric variable will give us a clearer idea of the distribution of the input variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVkm95RkRSW34APDFqnpVkl2APUbo882qenmf65LUZwYRSX2VZC/gRcApAFX1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\nMzNLjRMRMzMzS40TETMzM0vN/wEO/3iSnVmsMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b4d327470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fruits.drop('fruit_label', axis=1).plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False, figsize=(9,9), \n",
    "                                        title='Box Plot for each input variable')\n",
    "plt.savefig('fruits_boxplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fXsyw93HDhqSpkOQONEHj9Kp6x0LTVNVWYCvApk2banZ2dtn3nZubY6HpTjzp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\nYCUbAMBKNgCAVV/7rY3vLwkeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b40b31d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as pl\n",
    "fruits.drop('fruit_label' ,axis=1).hist(bins=30, figsize=(9,9))\n",
    "pl.suptitle(\"Histogram for each numeric input variable\")\n",
    "plt.savefig('fruits_hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like perhaps color score has a near Gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:3: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": 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TF/mXr47y0vlhoFA8863bE4SSWd7qmaCxwslLFwr7PE4rnzx0d2im2x/jB+eG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MXmmE0mTsdQMnE5gUxItGcBnzezzZJKCK3np6UrjiiWecB9wGXAOOAV4HkzezWdcUSxzAfOJvxd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\nORcTnpg555xzzsWEJ2bOOeecczHx/yPoiQIymUhpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b41fc7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "scatter_matrix(fruits.drop('fruit_label', axis=1), figsize=(10, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some pairs of attributes are correlated (mass and width). This suggests a high correlation and a predictable relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names = ['mass', 'width', 'height', 'color_score']\n",
    "X = fruits[feature_names]\n",
    "y = fruits['fruit_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:3: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": 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Spqahia6JRIKmpibmzh1+UvFEV2tGsAKYkSSHX7uGL7kIJln8OOa36WzMmIj0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b44d522e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "cmap = cm.get_cmap('gnuplot')\n",
    "scatter = pd.scatter_matrix(X, c = y, marker = 'o', s=40, hist_kwds={'bins':15}, figsize=(9,9), cmap = cmap)\n",
    "plt.suptitle('Scatter-matrix for each input variable')\n",
    "plt.savefig('fruits_scatter_matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply scalling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Models\n",
    "\n",
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression classifier on training set: 0.70\n",
      "Accuracy of Logistic regression classifier on test set: 0.40\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Logistic regression classifier on training set: {:.2f}'\n",
    "     .format(logreg.score(X_train, y_train)))\n",
    "print('Accuracy of Logistic regression classifier on test set: {:.2f}'\n",
    "     .format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 1.00\n",
      "Accuracy of Decision Tree classifier on test set: 0.73\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(clf.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting max decision tree depth to help avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 0.89\n",
      "Accuracy of Decision Tree classifier on test set: 0.73\n"
     ]
    }
   ],
   "source": [
    "clf2 = DecisionTreeClassifier(max_depth=3).fit(X_train, y_train)\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(clf2.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(clf2.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 0.95\n",
      "Accuracy of K-NN classifier on test set: 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of LDA classifier on training set: 0.86\n",
      "Accuracy of LDA classifier on test set: 0.67\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train, y_train)\n",
    "print('Accuracy of LDA classifier on training set: {:.2f}'\n",
    "     .format(lda.score(X_train, y_train)))\n",
    "print('Accuracy of LDA classifier on test set: {:.2f}'\n",
    "     .format(lda.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of GNB classifier on training set: 0.86\n",
      "Accuracy of GNB classifier on test set: 0.67\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "print('Accuracy of GNB classifier on training set: {:.2f}'\n",
    "     .format(gnb.score(X_train, y_train)))\n",
    "print('Accuracy of GNB classifier on test set: {:.2f}'\n",
    "     .format(gnb.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier on training set: 0.61\n",
      "Accuracy of SVM classifier on test set: 0.33\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "print('Accuracy of SVM classifier on training set: {:.2f}'\n",
    "     .format(svm.score(X_train, y_train)))\n",
    "print('Accuracy of SVM classifier on test set: {:.2f}'\n",
    "     .format(svm.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The KNN algorithm was the most accurate model that we tried. The confusion matrix provides an indication of one error made.\n",
    "\n",
    "Finally, the classification report provides a breakdown of each class by precision, recall, f1-score and support showing excellent results (However, the test set was small)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 0 0 0]\n",
      " [0 1 0 0]\n",
      " [0 0 8 0]\n",
      " [0 0 0 2]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       1.00      1.00      1.00         4\n",
      "          2       1.00      1.00      1.00         1\n",
      "          3       1.00      1.00      1.00         8\n",
      "          4       1.00      1.00      1.00         2\n",
      "\n",
      "avg / total       1.00      1.00      1.00        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "pred = knn.predict(X_test)\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the decision boundary of the k-nn classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.cm as cm\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "X = fruits[['mass', 'width', 'height', 'color_score']]\n",
    "y = fruits['fruit_label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "def plot_fruit_knn(X, y, n_neighbors, weights):\n",
    "    X_mat = X[['height', 'width']].as_matrix()\n",
    "    y_mat = y.as_matrix()\n",
    "\n",
    "    # Create color maps\n",
    "    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#AFAFAF'])\n",
    "    cmap_bold  = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#AFAFAF'])\n",
    "\n",
    "    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n",
    "    clf.fit(X_mat, y_mat)\n",
    "\n",
    "    # Plot the decision boundary by assigning a color in the color map\n",
    "    # to each mesh point.\n",
    "    \n",
    "    mesh_step_size = .01  # step size in the mesh\n",
    "    plot_symbol_size = 50\n",
    "    \n",
    "    x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1\n",
    "    y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, mesh_step_size),\n",
    "                         np.arange(y_min, y_max, mesh_step_size))\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.figure()\n",
    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "    # Plot training points\n",
    "    plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "\n",
    "    patch0 = mpatches.Patch(color='#FF0000', label='apple')\n",
    "    patch1 = mpatches.Patch(color='#00FF00', label='mandarin')\n",
    "    patch2 = mpatches.Patch(color='#0000FF', label='orange')\n",
    "    patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')\n",
    "    plt.legend(handles=[patch0, patch1, patch2, patch3])\n",
    "\n",
    "        \n",
    "    plt.xlabel('height (cm)')\n",
    "    plt.ylabel('width (cm)')\n",
    "    plt.title(\"4-Class classification (k = %i, weights = '%s')\"\n",
    "              % (n_neighbors, weights))\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RisDD0crAM3mgaFGmPvccTf38GO7ryxdA34AABgUFsXj0aPySsRY07kMrgeTR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b5994ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fruit_knn(X_train, y_train, 5, 'uniform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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67sfoZLBjx2osluTuvgD+/g9z+PAGj8ckEpLRwSJFOW2MQYzNxgdz5zJ91SqU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b44986080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fruit_knn(X_train, y_train, 1, 'uniform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7LxwhjLH3zBnW79nD8ZiYuD78wjj/h6+L81P9TZzjpWM/PZUBfo6Jodz+/YxZ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\nHsbSpuo+adgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b4eeea4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fruit_knn(X_train, y_train, 10, 'uniform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DagfGHJmg+ghE5ETgLuBY/31UtW+Y4jLmiFlnsjFVE+x8Qe8CLwIvAYfCF44x\noROpnck+n4+VK1eyc+dOjjnmGI4//nicS2qqZufOnaxcuZLU1FROO+00UlNTwxCtiQfBJoIiVX0h\nrJEYEwaRVjvYuHEjjz32NAcO1AYyUJ1Os2Z1+Otf/x+NGzcOqoyioiKee+5ffP31YpKS+gB78Ple\n5MYbr6VXr55hjd/EpsquLG4kIo2Aj0TkJhFpUbzMXW5MVIiEvoPc3FweeODv7Nr1EHl5K8jLe4f8\n/J/ZtOkKHnzwCXw+X1DlvPLKm3zzjVJY+Cu5ue+TmzuP/PwFjBv3BitX2kX6puoq6yxeCiwBrsbp\nI1jkLitebkzU8Loz+csvv6So6EzgGqC4KSgBn+9+9u2ry/LlyystIzc3l9mzZ1FQMBGo57fmVAoK\nHuLddz8Nedwm9lV2ZfFxACJSS1Xz/NfZXcVMtPKquWjNmo3k5fULsEYoKDiH9evX06lTp4D7FhYW\nsnjxYlatWoUzu0ugZqQsfvlldAgjNvEi2D6CRcDpQSwzJmrUdEJIT08jKekniooOX5ec/BMNGrQO\nuN+aNWt47LEnOXQog/z8zvh8O4C2wAeU/hf8mbS0BgHLMKYiFSYCEWkOHINzN7FO/F6fTcO5s5gx\nUc+/uSicSaFv315Mm3YvcDvO7TiK/RuYT9euh9+xNTc3l0ce+R9yciYCg/zWvAcMBH4CagP5pKY+\nxoAB1llsqq6yGkF/nAbNPwBP+S3fD9wfppiM8Uw4k0KzZs24+upLmTz5TIqKbsLn60hi4lckJk7k\ntttupE6dw8+tvvrqK3y+bpROAgAXA8/hdN1lUKvWWNq3P4qsLLu0x1RdZX0ErwKvisjFqvpeDcVk\njCc27NzJ5Llz2b5nDx3atuVPPXpQr5bTFRaqpDBgwHm0b9+OTz6Zw9atszjuuOacf/7jtGjRIuD2\nv/66lby8s8sprQ9NmrxMmzYrycoaTOfOnUlIsMkCTNVV1jT0/wI9LqaqT5VdZkw0ennWLO6eOJEr\nVDmxqIjPvvqKR958k08ffpgOxx4b0ppC69atuemm4UFt26xZOqmp35Gff/i61NTvGDZsIOeee261\n4jGmstOH+u5PF+BGnP6CY4AbgFPCG5oxNWPlpk3cN2kSiwsLebaoiFuA9/PzGXPwIH984vDx/TU5\nDLVHjx7A58DXZdYsQGQ+Z59dXm3BmOBVmAhU9RFVfQRnrNrpqnqHqt4BdMbpNzAm6v1r5kyuLyri\n+DLL/wTUy8lh3o8/BtyvJpJB/fr1ueuuUaSmnk9KylXAs6SmXklq6mDuvfe2gP0KxlRVsMNHWwEF\nfs8LgNYhj8aYGpJfWMg7X39/LYA9AAAT+0lEQVTNRwsX8t26dZzr85GHcx/WYgKcqsqGnTvLLacm\n5jPq3Lkz48f/k7lz57F58+e0bNmM3r3/SVpaWliPG6/279/PnDlzWLlyJbVr16ZHjx506tTpiOaD\nihbBJoLXgG9FZBqgwFBgctiiMiaMsnNyOPevf6Xujh1ck5/PQJybZvcAZgHFI/EVWCzCX44+2qtQ\nSzRo0IAhQwZ7HUbM27x5Mw899BAZGRn06NGD7OxsJk2axJdffsmoUaNitjM+2FtVPi4in+L8rwBc\nq6rfhS8sY8Ln4SlTOHnrViYWFZVcGHMlcB3OjbSfcZc9JUK9Ro3oduKJFZYXqbOcmqp7/vnnueii\ni7jgggtKlvXt25cHH3yQhQsX0rNnbF6nUdmooTRVzXYnmFvv/hSva6Sqe8IbnjGhpapMmjePpX5J\nAJxmoIeBk4HkhATmpaSQW78+Mx54IKabBMzvtm7dyrZt2+jfv3+p5ampqVx00UXMnDkzPhMB8CZw\nIc4kc+q3XNznbcIUlzFhccjnY19+fsAOrpZAgQgNhw3j0TZt6N+xY9BNAVYriH779u2jSZMmJCYm\nHrauWbNmrF+/nrvvvpuMjAwuuOCCoKcNjwaVjRq60P19nKq28fs5TlUtCZiok5SYyMmNGzM/wLqF\nwPFHHcUDF1/M+Z06xWx7sAnsmGOOYfPmzWRnZx+2bvny5bRu3Zprr72WoqIi7rrrLjZs2OBBlOER\n1CddRCaLyAgRaRfugIwJtzv++EdGpaay2W/ZFuDm1FTu/OMfvQrLeKx+/fr06NGDcePGke93Bd+6\ndev48MMP+fOf/8zJJ5/M8OHDufzyy3nppZc8jDa0gh01NAnoDjwnIm2AZcACVf1nuAIzJlyu6dOH\nLbt2kfHBB3RPSgJVvjx0iDsGDGB4VpbX4RkPXXvttbzwwguMGDGCU089le3bt7N9+3Zuuukm2rZt\nW7Jd3759ef3119m9ezfp6ekeRhwawY4amiMi84EzgD44Vxa3BywRmKgjIjxwySXceMEFzP7+ewAm\nZWSQXr9+lcopKCriymfHMu3/VlF0KI+UlPp069aOgj17WPPTT9SrXZvu/fpx4eDB1KpV/u071qxZ\nw1tvfcTq1atITa1NVtbZDB060C4W80BKSgq33nor27dvZ/Xq1bzyyis8/vjjtGrVqtR2SUlJ1KtX\nj5ycnJhIBME2Dc0GvgIuBVYDZ6iqNROZqNaoXj2GdevGsG7dqpwEfD4frW68m3e/KaLo0FTgJwoK\nnmP+/LXs+34Vy3NzeXfPHnLee4+/33cfBQUFActZunQpf/vbGL777jJycr7jt9+m88EHyt13P0Ju\nbm4IXqU5Es2aNaNnz5506NAh4J3jNm3aRG5uLs2bN/cgutALtjfsPzhXE2cAHYAMEakdtqiMiXDP\nzJjB9n0JwHycSnILYBiwmB9JJBun+vxeYSHNtm5l3ty5h5Xh8/kYO3YS+flvASNxZm05ncLCKezc\neRIzZ35RY6/HBDZ48GCmTp3K8uXLUXUGTu7YsYNnnnmGQYMGkRwjI8WCbRq6HUBE6gHXAhOB5kBq\n+EIzJnK9OGsRcDOlJ6UA58v8XP6Xj3gFZ5z1qPx8Hp89m35lxqevX7+evLwUoGy/hFBQMIo5c/4f\nQ4aUvQ+BqUlt27bltttu48UXXyQxMZG6deuyZcsWBg0axJAhQ7wOL2SCSgQicjPOVcWdgQ3AK8CX\nYYzLmIiWX+jDmZg3kDT8G3XqQcCmocLCQkTqAoEuWKtHYQ3NcGoq1qlTJ55//nl++eUX8vPzOe64\n46hdO7YaRIIdNVQb5w5lS1U1wB1XgyciJwFv+y1qA/xNVZ8pZxdjIs6gzicyduYk4C+U/iLPBT7i\nGr8lU5KTaX/mmYeV0bp1a1Q3AWuBE0qtS0x8iy5dbKb3SJGQkFBq1FCsCaqPQFX/V1UXVzcJuGWt\nVtXTVPU0nBpGDjCtuuUaU5OeuOIKkhNXAbcCv7lLNwADaYKP/jgp4UkRPkpNpZ/f3DXFUlNTGTZs\nKKmpg3FGZAPkITKW1NQ3GDz48H2MCYdgawThkgWsU9XYuUTPRKVA00MMKSyksKiIzXv20KBOHY6q\nV69kXb1atfj+yYfp9ujT/PbbBCAN2E+joxpzKO8Qf/Clkn3oECefeCKP3HwzDRs2DHjcoUMvJCUl\nibff7k9hYRKHDu2nbdsTGTnyoZiawsBENinuCffk4CKvAP9W1bEB1l0PXA/QqnHjzhvGjavp8Eyc\nCJQEfD4fH0ydyozp00n1+cg+dIiO7dtz9ciRNGnSpNS2O3fuZNu2bbRu3Zr69etTVFTErl27qFOn\nTtD3DDh06BC7du2iVq1aNGjQoPIdjAnC0KFDl6pql8q28ywRiEgKzpX97VV1e0XbdmnbVpeMHl0z\ngZm4Ut5EcW+88gobPv+cV/PzORk4APwjIYEJaWmMGTuWunXr1micxhyJYBOBl7NqnY9TG6gwCRgT\nDtOTk8tNAtnZ2Xz+2Wd87CYBcEb+POTzcXZODrO/sPH9JrZ4mQguB6Z4eHwTpyqbLnrlypWckZRE\n0wDrrigo4MfFi8MTmDEe8SQRiEgd4FzgfS+Ob+JXMPcMSE5O5mA56w4CSTFyNakxxTxJBKqao6rp\nqrrPi+Ob+BTsjWMyMjJYpcr3ZZYfAsampnKGzVBqYozdecPEharcPSwlJYWrR4ygX0oKk4FtwGJg\ncEoKua1akZmZGa4wjfGE19cRGBN25SWBAwcO8L07DXVGRgb1/WYg7d23L40aN+b5t97itl9+oUHd\nuvQ87zyuiqGJxowpZonAxLRASUBVeeedabz33gckJXUFhKKiF7joosFceulFJTer79ChAx06dKjh\niI2peZYITMwqryYwd+5cpk37PwoLV1BY+Ad36WamT+9HenoDzj33nJoL0pgIYH0EJiZV1Cfw1lsf\nk58/DmfK6GLHkJ//Am+/PSPssRkTaaxGYGJKZZ3Chw4dYufODUDvAGt7sGfPZgoLC60fwMQVqxGY\nmBHMyKCEhARSU+vhzBRa1iaSk1NJSrLzIxNfLBGYmBDs8FARoU+f3iQlPQz4z7OlJCU9TO/efUo6\ni42JF5YITNSryjUCAFdeeQnNm39Damof4HXgdVJT+9Ks2UKuuurSsMRoTCSzOrCJalVNAgB169bl\nH/94lK+++ooFCyagCj17nkb37jeSkpIShiiNiWyWCEzUOpIkUCwlJYU+ffrQp0+fEEZkTHSypiET\nlaqTBIwxpVkiMFHHkoAxoWWJwEQVSwLGhJ71EZioYAnAmPCxGoGJeJYEjAkvSwQmolkSMCb8LBGY\niGVJwJiaYYnARCRLAsbUHEsEJuJYEjCmZlkiMBHFkoAxNc8SgYkYlgSM8YYlAhMRLAkY4x27oMx4\nyhKAMd6zGoHxjCUBYyKDJQLjCUsCxkQOSwSmxlkSMCayWCIwNcqSgDGRxxJBCO3LyWHjrl0UFhV5\nHUpEsiRgTGSyUUMhsGn3bka8Pp45y34gqX4iyYWJ3HX+IO67cAgJCZZrjTGRzZNvKRFpKCJTRWSV\niKwUkW5exBEK+3JyOOOx+/ii738o2FpEztZ89i3I4b9XTeOOtyd7HZ4xxlTKq9PVfwKfqWo7oCOw\n0qM4qu2VeXPI7pbDoUd9UN9deArkfJLPi7O/YFd2tqfxRZIhhYVeh2CMCaDGE4GIpAE9gZcBVLVA\nVffWdByh8v7Kb8m5ouDwFU0g5awkFq5aVfNBGWNMFXhRI2gD7AQmish3IvIvEalbdiMRuV5ElojI\nkp0RfFZdKzEZcspZeRBSkqwbxhgT2bxIBEnA6cALqtoJOAjcW3YjVZ2gql1UtUuTtLSajjFo13Tu\nTd3nU+FQmRUrwPej0icjw5O4IpU1DxkTebxIBJuATaq62H0+FScxRKVLunWjfU5Lag1Ohm+BHcAb\nUKdfKs9cfg21U1K8DtEYYypU44lAVbcBv4rISe6iLODHmo4jVJKTkph318M80OAijhnWiLonptLt\n6ROZPvwuruvT1+vwjDGmUl41YI8C3hCRFOBn4FqP4giJ2ikpPDjkYh4ccrHXoUSFIYWFdnGZMRHE\nk0SgqsuALl4c20QGSwbGRA677NUYY+KcJQJjjIlzlgiMMSbOWSIwnrFrCoyJDJYIjDEmzlkiMJ6y\nWoEx3rNEYDxnycAYb1kiMMaYOGeJwBhj4pwlAhMRrHnIGO9YIjDGmDhnicAYY+KcJQITMax5yBhv\nWCIwxpg4Z4nAGGPinCUCEzHs/gTGeMMSgTHGxDlLBMaYahtSWGid/VHMEoGJCNYsZIx3LBEYz1kS\niB1WM4hOlgiMMSFnySC6WCIwnrLaQOyyZBA9LBEYz1gSiH2WDKKDJQJjTFhZMoh8lgiMMWFnySCy\nWSIwnrBmofhjySByWSIwNc6SQPyyZBCZLBEYY2qUJYPIY4nA1CirDRiwZBBpLBEYYzxhySByJHlx\nUBFZD+wHDgFFqtrFizhMzbLagDGRycsaQR9VPc2SgDHxy2oFkcGahkyNsNqAKY8lA++Jqtb8QUV+\nAX4DFBivqhMCbHM9cL379CRgdTUP2xjYVc0yalI0xRtNsUJ0xRtNsUJ0xRtNscKRxXusqjapbCOv\nEsHRqrpFRJoCXwCjVHVBmI+5JJqaoaIp3miKFaIr3miKFaIr3miKFcIbrydNQ6q6xf29A5gGnOlF\nHMYYYzxIBCJSV0TqFz8G+gE/1HQcxhhjHF4MH20GTBOR4uO/qaqf1cBxD+uHiHDRFG80xQrRFW80\nxQrRFW80xQphjNeTPgJjjDGRw4aPGmNMnLNEYIwxcS5uEoGIJIrIdyLysdexVERE1ovI9yKyTESW\neB1PZUSkoYhMFZFVIrJSRLp5HVMgInKS+54W/2SLyG1ex1UREbldRFaIyA8iMkVEankdU3lE5FY3\nzhWR+L6KyCsiskNEfvBb1khEvhCRte7vo7yMsVg5sQ5z31ufiIR8CGncJALgVmCl10EEKZqm3/gn\n8JmqtgM6EqHvsaqudt/T04DOQA7O0OWIJCLHALcAXVQ1A0gELvM2qsBEJAMYgTMMvCNwoYic4G1U\nh5kEnFdm2b3AbFU9AZjtPo8Ekzg81h+Ai4CwXG8VF4lARP4ADAD+5XUssURE0oCewMsAqlqgqnu9\njSooWcA6Vd3gdSCVSAJqi0gSUAfY4nE85TkZ+EZVc1S1CJgPDPU4plLcC1b3lFk8GHjVffwqMKRG\ngypHoFhVdaWqVnd2hXLFRSIAngHuBnxeBxIEBT4XkaXuNBuRrA2wE5joNrv9y702JNJdBkzxOoiK\nqOpm4ElgI7AV2Keqn3sbVbl+AHqKSLqI1AEuAFp6HFMwmqnqVgD3d1OP4/FMzCcCEbkQ2KGqS72O\nJUhnq+rpwPnASBHp6XVAFUgCTgdeUNVOwEEip3odkIikAIOAd72OpSJue/Vg4DjgaKCuiFzpbVSB\nqepK4H9wpov5DFgOFHkalKmSmE8EwNnAIPceCG8BfUXkdW9DKl+UTb+xCdikqovd51NxEkMkOx/4\nt6pu9zqQSpwD/KKqO1W1EHgfyPQ4pnKp6suqerqq9sRp1ljrdUxB2C4iLQDc3zs8jsczMZ8IVPU+\nVf2DqrbGaRKYo6oReWYVbdNvqOo24FcROcldlAX86GFIwbicCG8Wcm0EuopIHXEuw88iQjviAdwJ\nJBGRVjidmtHwHn8IXO0+vhr4wMNYPOXJHcpMubyafqM6RgFvuE0uPwPXehxPudz263OBv3gdS2VU\ndbGITAX+jdPM8h2RPSXCeyKSDhQCI1X1N68D8iciU4DeQGMR2QQ8BIwG3hGR63AS7zDvIvxdObHu\nAZ4DmgAzRGSZqvYP2TFtigljjIlvMd80ZIwxpmKWCIwxJs5ZIjDGmDhnicAYY+KcJQJjjIlzlghM\nzBCR1v4zNga5zw0iclUl21wjImPLWXd/BfuJiMxx52SqFhGZFSmzY5rYY4nAxDVVfVFVJ1ejiHIT\nAc6cO8tVNbsa5Rd7DbgpBOUYcxhLBCbWJIrIS+7c7Z+LSG0AEWkrIp+5k/l9KSLt3OUPi8id7uMz\nROQ/IvK1iPxvmdrF0e7+a0VkjLv9aJzZQZeJyBsBYrkCv6tVReQqt/zlIvKau2ySiLwgInNF5GcR\n6eXOR79SRCb5lfUhzlXRxoScJQITa04AnlfV9sBe4GJ3+QRglKp2Bu4ExgXYdyJwg6p2Aw6VWXca\ncClwKnCpiLRU1XuBXPc+B1cEKO9sYCmAiLQHHgD6qmpHnPtjFDsK6AvcDnwEPA20B04VkdMA3Ct1\nU92rd40JKZtiwsSaX1R1mft4KdBaROrhTNj2rjt9B0Cq/04i0hCor6qL3EVvAhf6bTJbVfe52/4I\nHAv8WkksjVR1v/u4LzBVVXcBqKr/fPMfqaqKyPfAdlX93j3OCqA1UPx6duDMRLq7kuMaUyWWCEys\nyfd7fAiojVPz3evenaw8UsG6QOUG879TJCIJqupzyy9vPpfisn1ljuMrc5xaQG4QxzWmSqxpyMQ8\nt7P2FxEZBiWjeTqW2eY3YL+IdHUXBXtbyEIRSS5n3Wqcm/eAcyvES4qbdkSkUVVegzsDaXNgfVX2\nMyYYlghMvLgCuE5ElgMrcG76UtZ1wAQR+RrnDH5fEOVOAP5TTmfxDJxZJFHVFcDjwHw3hqeqGH9n\nnNtB2g1fTMjZ7KPGuESknqoecB/fC7RQ1Vsr2a2i8loAk1X13BDE9k/gQ1WdXd2yjCnL+giM+d0A\nEbkP5/9iA3BNdQpT1a3uUNa0EFxL8IMlARMuViMwxpg4Z30ExhgT5ywRGGNMnLNEYIwxcc4SgTHG\nxDlLBMYYE+f+P7yl9K7M2BjYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b47b2b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fruit_knn(X_test, y_test, 5, 'uniform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x24b4be71c18>,\n",
       "  <matplotlib.axis.XTick at 0x24b4be7d5c0>,\n",
       "  <matplotlib.axis.XTick at 0x24b4be717f0>,\n",
       "  <matplotlib.axis.XTick at 0x24b47795898>,\n",
       "  <matplotlib.axis.XTick at 0x24b47795ef0>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b4be6d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_range = range(1, 20)\n",
    "scores = []\n",
    "\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors = k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    scores.append(knn.score(X_test, y_test))\n",
    "plt.figure()\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('accuracy')\n",
    "plt.scatter(k_range, scores)\n",
    "plt.xticks([0,5,10,15,20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this particular dateset, we obtain the highest accuracy when k=5."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
